Application of swarm intelligence optimization for enhancing detection of epileptic seizures in EEG signals / Asmaa Hamad Elsaied Mohamed ; Supervised Aly Aly Fahmy , Aboulella Hassanien , Essam Halim Houssein

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Asmaa Hamad Elsaied Mohamed

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TextLanguage: English Publication details: Cairo : Asmaa Hamad Elsaied Mohamed , 2018Description: 89 Leaves : charts ; 30cmOther title:

تطبيق امثلية الذكاء السربي لتحسين نوبات اكتشاف الصرع في إشارات رسم المخ [Added title page title]

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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Computer Science Summary: The thesis introduces a hybrid classification model using swarm optimization algorithms and support vector machines (SVMs) for automatic seizure detection in EEG. This proposed classification model consists of four main phases; namely,1) EEG pre-processing used to remove the noises from the EEG signals and decompose EEG signal into various sub-bands,2) feature extraction used to extract the EEG signal features from decomposed signal,3) Feature selection and classifier Parameters Optimization based swarm algorithms and 4) classification phase that is mainly used to analyze and classify the EEG signal into normal or abnormal

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Item type	Current library	Home library	Call number	Copy number	Status	Barcode
Thesis	قاعة الرسائل الجامعية - الدور الاول	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.20.03.M.Sc.2018.As.A (Browse shelf(Opens below))		Not for loan	01010110077119000
CD - Rom	مخـــزن الرســائل الجـــامعية - البدروم	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.20.03.M.Sc.2018.As.A (Browse shelf(Opens below))	77119.CD	Not for loan	01020110077119000

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Previous	Cai01.20.03.M.Sc.2017.Sh.E Enhancing cloud services provisioning for social Networks /	Cai01.20.03.M.Sc.2018.Ah.C Clickjacking defense technique /	Cai01.20.03.M.Sc.2018.Ah.C Clickjacking defense technique /	Cai01.20.03.M.Sc.2018.As.A Application of swarm intelligence optimization for enhancing detection of epileptic seizures in EEG signals /	Cai01.20.03.M.Sc.2018.As.A Application of swarm intelligence optimization for enhancing detection of epileptic seizures in EEG signals /	Cai01.20.03.M.Sc.2018.Eh.C Comparison and enhancement of hyperspectral unmixing algorithms and techniques /	Cai01.20.03.M.Sc.2018.Eh.C Comparison and enhancement of hyperspectral unmixing algorithms and techniques /	Next

Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Computer Science

The thesis introduces a hybrid classification model using swarm optimization algorithms and support vector machines (SVMs) for automatic seizure detection in EEG. This proposed classification model consists of four main phases; namely,1) EEG pre-processing used to remove the noises from the EEG signals and decompose EEG signal into various sub-bands,2) feature extraction used to extract the EEG signal features from decomposed signal,3) Feature selection and classifier Parameters Optimization based swarm algorithms and 4) classification phase that is mainly used to analyze and classify the EEG signal into normal or abnormal

Issued also as CD

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